Lead Software Engineer - AI & Machine Learning

You will influence outcomes across a federated delivery model by enabling reuse, aligning stakeholders, and embedding practical frameworks that improve delivery speed, resiliency, and control adherence.

As a Lead Software Engineer at JPMorganChase within Corporate Technology and the AI/Machine Learning & Automation Center of Excellence, you will drive scalable adoption of responsible AI and intelligent automation by defining standards, reference architectures, and governance that teams can implement consistently.

Job Responsibilities


* Drive enterprise alignment and adoption of AI/machine learning and intelligent automation by partnering with engineering, product, and business teams to embed consistent approaches into delivery roadmaps.


* Influence cross-functional stakeholders to align priorities across federated teams, reduce fragmentation, and drive adherence to Center of Excellence frameworks and standards.


* Define, maintain, and evolve scalable reference architectures, implementation patterns, and standards that accelerate adoption while remaining practical for delivery teams.


* Enable reuse at scale by promoting shared tooling, accelerators, and reusable capabilities that improve time-to-market and consistency.


* Provide advisory support and constructive challenge to delivery teams to strengthen design decisions, implementation quality, and operational readiness of AI-enabled solutions.


* Establish and reinforce Responsible AI governance across distributed teams, including practices for ethical AI use, bias mitigation, data quality, and lifecycle management.


* Promote secure, resilient, and scalable engineering practices aligned to enterprise architecture expectations across cloud, APIs, and integration patterns.


* Lead and contribute to a community of practice to uplift capability, share patterns, and drive continuous improvement across teams.


* Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.


* Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Required Qualifications, Capabilities, and Skills


* Formal training or certification on software engineering concepts and 5+ years applied experience.


* Demonstrated ability to influence and align senior stakeholders across multiple teams in a large, complex organization without direct authority.


* Experience operating within an enterprise enablement function or center of excellence m...